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Joined 9 months ago
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Cake day: March 22nd, 2024

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  • I mean, if you’re talking specifically in context about people with vaginas instead of women then using the gendered term does exclude both women without vaginas and men with them who are probably a relevant group in that context. But seriously how often does that come up for you? How often is the most important part of the woman you’re referring to her anatomy?

    And while “females” is probably just as accurate in most contexts it’s also been poisoned with incel vibes at this point and it’s gonna be some time before it can be salvaged for general use outside of specific biological contexts without sounding like you’re about to unload a whole lot of baggage into the thread instead of getting therapy.















  • Okay apparently it was my turn to subject myself to this nonsense and it’s pretty obvious what the problem is. As far as citations go I’m gonna go ahead and fall back to “watching how a human toddler learns about the world” which is something I’m sure most AI researchers probably don’t have experience with as it does usually involve interacting with a woman at some point.

    In the real examples that he provides, the system isn’t “picking up the wrong goal” as an agent somehow. Instead it’s seeing the wrong pattern. Learning “I get a pat on the head for getting to the bottom-right-est corner of the level” rather than “I get a pat on the head when I touch the coin.” These are totally equivalent in the training data, so it’s not surprising that it’s going with the simpler option that doesn’t require recognizing “coin” as anything relevant. This failure state is entirely within the realms of existing machine learning techniques and models because identifying patterns in large amounts of data is the kind of thing they’re known to be very good at. But there isn’t any kind of instrumental goal establishing happening here as much as the system is recognizing that it should reproduce games where it moves in certain ways.

    This is also a failure state that’s common in humans learning about the world, so it’s easy to see why people think we’re on the right track. We had to teach my little on the difference between “Daddy doesn’t like music” and “Daddy doesn’t like having the Blaze and the Monster Machines theme song shout/sang at him when I’m trying to talk to Mama.” The difference comes in the fact that even as a toddler there’s enough metacognition and actual thought going on that you can help guide them in the right direction, rather than needing to feed them a whole mess of additional examples and rebuild the underlying pattern.

    And the extension of this kind of pattern misrecognition into sci-fi end of the world nonsense is still unwarranted anthropomorphism. Like, we’re trying to use evidence that it’s too dumb to learn the rules of a video game as evidence that it’s going to start engaging in advanced metacognition and secrecy.



  • I mean a lot of the services that companies are using are cloud-hosted, meaning that especially if you have branch offices or a lot of remote workers a normal firewall in the datacenter introduces an unnecessary bottleneck. Putting the logical edge of your organization’s network in the cloud too makes sense from a performance perspective in that case, and then turning the actual firewalls into SaaS seems much less absurd.


  • Brief overlapping thoughts between parenting and AI nonsense, presented without editing.

    The second L in LLM remains the inescapable heart of the problem. Even if you accept that the kind of “thinking” (modeling based on input and prediction of expected next input) that AI does is closely analogous to how people think, anyone who has had a kid should be able to understand the massive volume of information they take in.

    Compare the information density of English text with the available data on the world you get from sight, hearing, taste, smell, touch, proprioception, and however many other senses you want to include. Then consider that language is inherently an imperfect tool used to communicate our perceptions of reality, and doesn’t actually include data on reality itself. The human child is getting a fire hose of unfiltered reality, while the in-training LLM is getting a trickle of what the writers and labellers of their training data perceive and write about. But before we get just feeding a live camera and audio feed, haptic sensors, chemical tests, and whatever else into a machine learning model and seeing if it spits out a person, consider how ambiguous and impractical labelling all that data would be. At the very least I imagine the costs of doing so are actually going to work out to be less efficient than raising an actual human being and training them in the desired tasks.

    Human children are also not immune to “hallucinations” in the form of spurious correlations. I would wager every toddler has at least a couple of attempts at cargo cult behavior or inexplicable fears as they try to reason a way to interact with the world based off of very little actual information about it. This feeds into both versions of the above problem, since the difference between reality and lies about reality cannot be meaningfully discerned from text alone and the limited amount of information being processed means any correction is inevitably going to be slower than explaining to a child that finding a “Happy Birthday” sticker doesn’t immediately make it their (or anyone else’s) birthday.

    Human children are able to get human parents to put up with their nonsense ny taking advantage of being unbearably sweet and adorable. Maybe the abundance of horny chatbots and softcore porn generators is a warped fun house mirror version of the same concept. I will allow you to fill in the joke about Silicon Valley libertarians yourself.

    IDK. Felt thoughtful, might try to organize it on morewrite later.